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Comparison of beta peak detection algorithms for data-driven deep brain stimulation programming strategies in Parkinson’s disease
npj Parkinson's Disease ( IF 6.7 ) Pub Date : 2024-08-09 , DOI: 10.1038/s41531-024-00762-7
Sunderland K Baker 1 , Erin M Radcliffe 2, 3 , Daniel R Kramer 2 , Steven Ojemann 2, 4 , Michelle Case 5 , Caleb Zarns 5 , Abbey Holt-Becker 5 , Robert S Raike 5 , Alexander J Baumgartner 2, 4 , Drew S Kern 2, 4 , John A Thompson 2, 4, 6
Affiliation  

Oscillatory activity within the beta frequency range (13–30 Hz) serves as a Parkinson’s disease biomarker for tailoring deep brain stimulation (DBS) treatments. Currently, identifying clinically relevant beta signals, specifically frequencies of peak amplitudes within the beta spectral band, is a subjective process. To inform potential strategies for objective clinical decision making, we assessed algorithms for identifying beta peaks and devised a standardized approach for both research and clinical applications. Employing a novel monopolar referencing strategy, we utilized a brain sensing device to measure beta peak power across distinct contacts along each DBS electrode implanted in the subthalamic nucleus. We then evaluated the accuracy of ten beta peak detection algorithms against a benchmark established by expert consensus. The most accurate algorithms, all sharing similar underlying algebraic dynamic peak amplitude thresholding approaches, matched the expert consensus in performance and reliably predicted the clinical stimulation parameters during follow-up visits. These findings highlight the potential of algorithmic solutions to overcome the subjective bias in beta peak identification, presenting viable options for standardizing this process. Such advancements could lead to significant improvements in the efficiency and accuracy of patient-specific DBS therapy parameterization.



中文翻译:


帕金森病数据驱动的深部脑刺激编程策略的 β 峰值检测算法的比较



β 频率范围 (13–30 Hz) 内的振荡活动可作为帕金森病的生物标志物,用于定制深部脑刺激 (DBS) 治疗。目前,识别临床相关的 β 信号,特别是 β 频谱带内峰值振幅的频率,是一个主观过程。为了告知客观临床决策的潜在策略,我们评估了识别 β 峰的算法,并为研究和临床应用设计了标准化方法。采用一种新颖的单极参考策略,我们利用大脑传感装置来测量沿植入丘脑底核的每个 DBS 电极的不同接触点的 β 峰值功率。然后,我们根据专家共识建立的基准评估了十种 beta 峰值检测算法的准确性。最准确的算法都共享相似的基础代数动态峰值幅度阈值方法,在性能上符合专家共识,并在随访期间可靠地预测临床刺激参数。这些发现凸显了算法解决方案克服 β 峰识别中的主观偏差的潜力,为标准化该过程提供了可行的选择。这些进步可能会显着提高患者特定 DBS 治疗参数化的效率和准确性。

更新日期:2024-08-09
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